In SAC, an ensemble of \(n\) (=2) \(Q\) -value networks are used to tame overestimation bias [1]}, [2]}.
If each \(Q\) -value network also performed \(F\) -value prediction, we conveniently have an ensemble of \(F\) -value predictors.
We thus have \(\Lambda _i(Q^\pi , \lbrace \mathbf {s}_t, \mathbf {a}_t\rbrace )\)  for \(i \in \lbrace 0, 1, ..., n-1\rbrace \)  per \(\lbrace \mathbf {s}_t, \mathbf {a}_t\rbrace \)  pair.
We then propose intentionally overestimating \(\Delta (Q^\pi , \lbrace \mathbf {s}_t, \mathbf {a}_t, \bar{\mathbf {a}}_t\rbrace )\)  by taking the maximal difference according to the ensemble:
\(\begin{aligned}\hspace{20.0pt}&\hspace{-20.0pt}\Delta (Q^\pi , \lbrace \mathbf {s}_t, \mathbf {a}_t, \bar{\mathbf {a}}_t\rbrace ) = \\& \max _i[\Lambda _i(Q^\pi , \lbrace \mathbf {s}_t, \bar{\mathbf {a}}_t\rbrace )] - \min _i[\Lambda _i(Q^\pi , \lbrace \mathbf {s}_t, \mathbf {a}_t\rbrace )]\end{aligned}\) 
